LDA Archetype Outlier Analytics for Real-Time Fraud Detection
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Solution Overview
Problem
Existing fraud detection models in the financial industry face challenges in real-time anomaly detection due to the lack of high-quality transaction tags, leading to poor performance in unsupervised models.
Innovation Solution
The system employs a Latent Dirichlet Allocation (LDA) model to identify archetypes in transaction data, dividing it into subsets based on probability distributions, and uses a self-calibrating outlier model to score transactions without relying on tags, enabling real-time detection of anomalies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If supervised models are used with transaction tags, then detection precision is improved, but data collection difficulty increases due to lack of high-quality tags
Solution Approach 1:
The patent introduces an intermediary process that uses unsupervised learning to generate pseudo-tags from transaction data without requiring manual annotation. This intermediary step transforms raw transaction features into structured labels that can then be used by supervised models, bridging the gap between untagged data and tag-dependent detection algorithms.
Solution Approach 2:
The patent segments the transaction detection process into multiple stages: first using unsupervised learning to identify patterns and generate pseudo-tags, then using these pseudo-tags to train supervised models. This segmentation allows the system to leverage both unsupervised pattern recognition and supervised precision without requiring complete manual tagging of all data.
2Ease of operation
If unsupervised models are used without tags, then ease of operation is improved, but detection precision deteriorates due to lack of transaction tags
Solution Approach 1:
The system employs self-service mechanisms where the unsupervised model automatically generates pseudo-tags from the transaction data itself without external intervention. These self-generated labels are then used to refine and improve the model's detection capabilities, allowing the system to bootstrap its own performance improvement cycle.
Solution Approach 2:
The patent implements a feedback loop where the unsupervised model's outputs are evaluated and used to generate pseudo-tags, which then feed back into training improved detection models. This continuous feedback mechanism allows the system to progressively improve detection precision while maintaining ease of operation by avoiding manual tag creation.
3Device complexity
If traditional self-calibrating outlier models are used, then device complexity is reduced, but detection precision deteriorates in real-time scenarios
Solution Approach 1:
The patent applies preliminary action by pre-processing transaction data through unsupervised learning to extract meaningful patterns and generate pseudo-tags before the actual detection process. This preliminary step creates a more informative feature space that enhances subsequent detection precision without significantly increasing overall system complexity.
Solution Approach 2:
The system combines multiple analytical approaches into a composite detection framework, merging unsupervised pattern recognition with supervised classification and outlier detection. This composite approach leverages the strengths of each method while maintaining manageable complexity through modular architecture.
Data Source
AI summary
The current subject matter describes a method and system of detecting frauds or anomalous behavior. The procedures include extracting characteristics from a dataset to generate words and documents, executing a topic model to obtain the respective probabilities of appearance of a document in each latent archetype, dividing the dataset into a plurality of subsets based upon the archetypes. The formed subsets are further utilized to estimate the quantiles and calculate scores using a self-calibrating outlier model. The score of each new transaction is determined based on a single archetype or based on the sum of weighted scores determined from all the archetypes and associated statistics. Such methods are superior to a simple self-calibration outlier model without an LDA archetype. The detection system with the LDA archetypes and self-calibrating outlier model is implemented with the sliding window technique incorporating new transactions into the topic model and it is capable of operating in real-time for the purpose of identifying frauds and outliers.